This document explores qualitative indicators from an ActivityInfo database that is monitoring Ecuador.
| Indicator count totals | |||||
|---|---|---|---|---|---|
| Nov 2013 to May 2019 | |||||
| Date | Quantity | Select | Single-line text | Multi-line text | % of total data collected |
| Nov 2013 | 141,442 | 30,531 | 0 | 6,309 | 3.54% |
| June 2015 | 1,887,857 | 745,841 | 85,863 | 57,128 | 2.06% |
| Sept 2016 | 3,380,991 | 1,296,548 | 191,640 | 116,184 | 2.33% |
| May 2017 | 4,932,977 | 1,809,419 | 265,196 | 168,599 | 2.35% |
| May 2019 | 12,174,327 | 7,595,829 | 2,683,945 | 915,948 | 3.92% |
From the perspective of ActivityInfo, it shows a clear need for new tools to support analysis of qualitative data as the absolute volume of qualitative data has increased by a factor of 150, and almost doubled as a relative share of all data collected.
The data has been extracted from ActivityInfo and pre-processed to make it ready for the analysis. Source the R/etl.R file to download the raw data.
Read the data from the source that has been extracted, cleaned, and transformed. Select the rows where the field type equals to NARRATIVE, this indicates that is a multi-line text field in ActivityInfo. Select these columns and analyze them by comparing and contrasting with other fields types associated with the textual field types.
The table below shows partner count per each record:
ACNUR has nine hundred sixty-eight records, which is 62.0% of the total records.
Second, NRC has one hundred thirty-four records, which is 8.60% of the total records.
The most difference between percentages of the partners ACNUR and NRC is 53%.
| partnerName | freq | prop |
|---|---|---|
| ACNUR | 968 | 0.620 |
| NRC | 134 | 0.086 |
| PMA | 106 | 0.068 |
| UNICEF | 84 | 0.054 |
| OIM | 73 | 0.047 |
| UNFPA | 64 | 0.041 |
| CARE | 29 | 0.019 |
| Dialogo Diverso | 26 | 0.017 |
| Mision Scalabriniana | 18 | 0.012 |
| ADRA | 15 | 0.010 |
| RET | 15 | 0.010 |
| PNUD | 7 | 0.004 |
| JRS Ecuador | 5 | 0.003 |
| OPS/OMS | 5 | 0.003 |
| Plan Internacional | 5 | 0.003 |
| World Vision | 5 | 0.003 |
| UNESCO | 3 | 0.002 |
The table below shows the proportion of records entered by partners and sub-partners.
676 out of 968 total responses of ACNUR is actually coming from HIAS.
UNICEF has more diversed partners in terms of reporting. 44% of responses of UNICEF comes from HIAS. 25% of reporting comes from the UNICEF itself.
Under PMA, there are 13 sub-partners. HIAS reports 41% of these records.
Those are the total numbers of reporting in all database, the numbers are not specific to the narratives (multi-line text fields). In the next section, we count the number of reportings done only in the narrative sections.
| subPartnerName | freq | prop | percent |
|---|---|---|---|
| ACNUR | |||
| HIAS | 676 | 0.698 | 69% |
| ACNUR | 284 | 0.293 | 29% |
| JRS Ecuador | 5 | 0.005 | 0% |
| Federación de Mujeres de Sucumbios | 2 | 0.002 | 0% |
| Federación de mujeres de Sucumbíos | 1 | 0.001 | 0% |
| NRC | |||
| NRC | 134 | 1.000 | 100% |
| OIM | |||
| OIM | 73 | 1.000 | 100% |
| UNFPA | |||
| UNFPA | 62 | 0.969 | 96% |
| RET | 2 | 0.031 | 3% |
| PMA | |||
| HIAS | 44 | 0.415 | 41% |
| ADRA | 10 | 0.094 | 9% |
| Buen Pastor | 5 | 0.047 | 4% |
| Fundación de Mujeres de Sucumbios | 5 | 0.047 | 4% |
| Fundación Tarabita | 5 | 0.047 | 4% |
| Hermanas Salesias | 5 | 0.047 | 4% |
| Hogar de Cristo | 5 | 0.047 | 4% |
| Pastoral Social Cáritas Tulcán | 5 | 0.047 | 4% |
| SJR | 5 | 0.047 | 4% |
| World Vision | 5 | 0.047 | 4% |
| Alas de Colibri | 4 | 0.038 | 3% |
| Casa Matilde | 4 | 0.038 | 3% |
| Patronato | 4 | 0.038 | 3% |
| UNICEF | |||
| HIAS | 37 | 0.440 | 44% |
| ADRA | 21 | 0.250 | 25% |
| UNICEF | 21 | 0.250 | 25% |
| NRC | 3 | 0.036 | 3% |
| Centro de Desarrollo y Autogestión | 2 | 0.024 | 2% |
| CARE | |||
| CARE | 29 | 1.000 | 100% |
| Dialogo Diverso | |||
| Dialogo Diverso | 25 | 0.962 | 96% |
| OIM | 1 | 0.038 | 3% |
| Mision Scalabriniana | |||
| Mision Scalabriniana | 18 | 1.000 | 100% |
| ADRA | |||
| ADRA | 15 | 1.000 | 100% |
| RET | |||
| RET | 15 | 1.000 | 100% |
| PNUD | |||
| PNUD | 7 | 1.000 | 100% |
| JRS Ecuador | |||
| JRS Ecuador | 5 | 1.000 | 100% |
| OPS/OMS | |||
| OPS/OMS | 5 | 1.000 | 100% |
| Plan Internacional | |||
| Plan Internacional | 5 | 1.000 | 100% |
| World Vision | |||
| World Vision | 5 | 1.000 | 100% |
| UNESCO | |||
| UNESCO | 3 | 1.000 | 100% |
In this section, we focus on a subset of the reports, which do particularly have the multi-text fields, called “Narrative data” in ActivityInfo terms. Plain saying that narrative data is multi-line text fields allowing users to enter long texts.
As we have seen previously, Not all partners (and sub-partners) enter narrative records. For instance, the partner PMA has lots of sub-partners reporting for the different data types but there are no narratives there.
In terms of narrative data,
TODO The partners X and Y is like that. The rest of the main partners, namely … do not have any subpartners reporting by them.
| Canton and provinces | |||
|---|---|---|---|
| The number of reports in the multi-text (narrative) fields | |||
| canton | freq | canton.prop | province.prop |
| PICHINCHA | |||
| QUITO | 126 | 1.000 | 0.190 |
| CARCHI | |||
| TULCAN | 101 | 1.000 | 0.153 |
| SUCUMBIOS | |||
| LAGO AGRIO | 89 | 1.000 | 0.134 |
| IMBABURA | |||
| IBARRA | 76 | 1.000 | 0.115 |
| GUAYAS | |||
| GUAYAQUIL | 55 | 1.000 | 0.083 |
| EL ORO | |||
| HUAQUILLAS | 49 | 0.860 | 0.074 |
| MACHALA | 8 | 0.140 | 0.012 |
| ESMERALDAS | |||
| ESMERALDAS | 38 | 0.551 | 0.057 |
| SAN LORENZO | 29 | 0.420 | 0.044 |
| ELOY ALFARO | 2 | 0.029 | 0.003 |
| SANTO DOMINGO DE LOS TSACHILAS | |||
| SANTO DOMINGO | 38 | 1.000 | 0.057 |
| AZUAY | |||
| CUENCA | 30 | 1.000 | 0.045 |
| COTOPAXI | |||
| LATACUNGA | 4 | 1.000 | 0.006 |
| LOS RIOS | |||
| QUEVEDO | 4 | 1.000 | 0.006 |
| TUNGURAHUA | |||
| BAÑOS DE AGUA SANTA | 4 | 0.571 | 0.006 |
| AMBATO | 3 | 0.429 | 0.005 |
| CHIMBORAZO | |||
| RIOBAMBA | 3 | 1.000 | 0.005 |
| MANABI | |||
| MANTA | 2 | 1.000 | 0.003 |
| BOLIVAR | |||
| SAN MIGUEL | 1 | 1.000 | 0.002 |
Treemap plot showing canton and province reporting frequencies.
First of all, we shorten the names and therefore re code form topics because they appear to be too long and disarray the plots. The re coded table below provides a look up for form labels and their abbreviations:
| labelFormsRecode | labelForms |
|---|---|
| Salud | Salud |
| Agua | Agua, saneamiento e higiene |
| Alojamiento | Alojamiento Temporal |
| Necesidades | Necesidades básicas/Otro |
| Población | Manejo de la información y entrega directa de la información a la población |
| Socios | Manejo de la información para socios y análisis de las necesidades |
| VBG | Protección_VBG |
| Tráfico | Trata_y_tráfico |
| Educación | Acceso_a_educación |
| Hábitat | Acceso a vivienda y hábitat dignos en comunidades receptoras |
| Técnico | Medios de vida y formación técnico-profesional |
| SocialCohesión | Cohesión_social |
| Educacional | Apoyo Educacional a Comunidades Receptoras |
| VBG_SSR | Asistencia técnica para VBG-SSR |
| Fronteras | Asistencia técnica para protección/gestión de fronteras |
| Coordinacion | Asistencia técnica para gestion de la informacion y coordinacion |
| SectorLaboral | Asistencia técnica para el sector laboral |
| Protección | Asistencia técnica para protección |
| ProtecciónInfancia | Asistencia técnica para protección de la infancia |
| LGBTI | Protección_LGBTI |
Response quality means how much response the questions receive. The idea is to find relations that affect the response quality to understand if they work or not under some conditions.
Research questions:
What is the quality of textual responses in the narrative fields?
Is there any relationship between the word counts of response, question and description fields?
What is the distribution between response word count and explanatory variables such as the question, form topic, canton name, partner name, etc.
Assumptions:
In other words, we assume that the more word the better is. The limitations are based on the unequal distribution of the data. The word count of responses and questions can be related to other things, such as the questions require short answers so then the responses tend to be shorter.
Additionally, we can have a cross-analysis to test these outcomes. It might be a good idea to have a small subset of data and ask an expert to test the assumptions qualitatively. For instance, we can take the first twenty responses with the highest word count and the last twenty responses with the lowest word count. We chose the extreme directions because they point out the greatest differences which are easier to test assumptions.
One issue with the nature of the questions is that they are only unique in a form. These questions can be distributed across multiple forms. The questions sharing the same name will have different meanings. For instance, the question “Cualitativo” from the form “Salud” should imply different thing than the question “Cualitativo” from the form “Protección_VBG”.
In order to solve this kind of problem:
We can combine question with the form and also its folder label. There we can achieve a unique name for each question.
Another thing to resolve this would be doing analysis to move the analysis up to form level. In this file, we did both, therefore the analysis shown as below:
Count of responses per topic/question:
| labelForms | question | response | .responseWordCount | .questionWordCount | partnerName | canton | description | labelFormsRecode |
|---|---|---|---|---|---|---|---|---|
| Salud | Cualitativo | 1. Entrega de k | 302 | 1 | UNFPA | TULCAN | Descripción de | Salud |
| Salud | Cualitativo | 1. Entrega de k | 302 | 1 | UNFPA | HUAQUILLAS | Descripción de | Salud |
| Salud | Cualitativo | 1. Entrega de k | 302 | 1 | UNFPA | MACHALA | Descripción de | Salud |
| Salud | Cualitativo | 1. Entrega de k | 302 | 1 | UNFPA | LAGO AGRIO | Descripción de | Salud |
| Salud | Cualitativo | Se complementa | 13 | 1 | UNFPA | LAGO AGRIO | Descripción de | Salud |
| Salud | Cualitativo | 233 Equipos méd | 46 | 1 | UNFPA | SAN LORENZO | Descripción de | Salud |
It’s also a good practice to see the number of questions. For example, one question has two responses, therefore they’re short. Therefore, jittered points are added to give a glance about the number of observations in the same plot.
In the plot above, the box plot of form topics and response word counts based on the raw data, the outliers are shown in orange color. Outliers are the points placed outside the whiskers, which is the long line, of the boxplot.
The response word count distribution per form topic categorized by partner name:
The response word count distribution per form topic categorized by canton name:
A caveat: Reducing multiple values down to a single value should be avoided in the early stages of the analysis because reducing hides a lot e.g. a bar chart showing average the word count per partner. Some partners may write longer than others, because:
They actually write longer than other partners.
The questions they answered require short answers.
Some questions have the description field giving extra details about the questions.
Do some questions with the extra description field have better response quality than the questions which do not have it?
Looking at the table containing form name, question, description and so on:
We see in the plot below that the response word counts per form and colored if a response has a description field or not. Having a description field or not is calculated as that a description field has a minimum one word.
The responses with the longest word counts are the ones with description. Nevertheless, it is not so easy to see a clear trend that there’s a correlation between response word count and description fields. Interestingly, the form topic Protección_VBG has no description fields at all in its form topics.
TODO ANOVA
TODO
TODO
TODO
We can look at multiple continuous variables in our data.
word count of response field: the dependent variable.
word count of question field: an independent variable.
word count of description field: an independent variable.
Scatter plots help understand the characteristics of those variables. However, we miss a general understanding that is the trend line.
The gray area around the lines shows the confidence band at the 0.95 level. Although there’s a straight slope in the linear regression line, we cannot say that the trend line is robust because the confidence band representing the uncertainty in the estimate is wide.
TODO
In that section, we take text as data.
Describe how to prepare textual data and what common steps are usually performed.
They are usually four steps involved in this process:
1. Tokenization
Tokenization means to split a text into tokens considered meaningful units of text. A token can either be a word (and often it is) or a group of words (such as bigram), or even a sentence that depends on the level of analysis.
| labelFolder | labelForms | Month | question | description | partnerName | subPartnerName | province | canton | labelFormsRecode | word |
|---|---|---|---|---|---|---|---|---|---|---|
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | 1 |
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | entrega |
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | de |
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | kits |
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | de |
| Objectivo_1.1 | Salud | 2019-02 | Cualitativo | Descripción de procedimientos elaborados de referencia y contrareferencia de emergecisa obstétrica y neonatal, y otros problemas SSR | UNFPA | UNFPA | CARCHI | TULCAN | Salud | salud |
Perform stemming, which you bring nouns/verbs back to infinitive forms, and tokenization, which is separating words in meaningful pieces.
2. Strip punctuation
Punctuation is often not required in text analysis (unless a researcher wants to tokenize the text based on a specific classifier such as sentence tokens); therefore, they create noise.
3. Convert text into lowercase
When the text turned into lowercase, for instance, the words respuesta and Respuesta will no longer be taken as different words.
4. Exclude stopwords & numbers
Stop words usually mean the most common words in a language that will bring no significant results in analysis. They are overly distributed in the text and they will not give so meaningful results itself. Stop-words are including articles (el/la), conjunctions (y), pronouns (yo/tú/etc.) and so on.
In text mining, this process is usually done after the text converted into lowercase so one does not have to provide stop words including both lower and sentence case versions.
We import a list of Spanish stopwords data (source here) and perform a filtering join returning tokens from textual data by excluding the words listed in the stopwords. that only returns the tokens not listed in the stopwords.
It’s also possible to add more custom words such as ACNUR or HIAS if such organization names are not desired in the results.
The original tokens had 37845 rows but after stop words, it decreased to 19221 and that the change in between is 51%.
The most common words in all responses:
Sentiment analysis (also called as opinion mining) is a technique to understand the emotional meanings of text given by a dictionary describing the positive/negative words that already done by humans.
The responses seem to be written with a formal tone of voice; therefore, the responses may not show any sentiment at all.
First, we find a sentiment lexicon for the Spanish language (source here).
A wordcloud showing positive and negative words:
Silge J, Robinson D (2017). Text mining with R: A tidy approach. O’Reilly Media, Inc.
SocialCohesión